Joint-octamamba:an octa joint segmentation network based on feature enhanced mamba
On the map: State-space sequence models (S4, Mamba) → Mamba for medical image analysis
Abstract (arXiv)
OCTA is a crucial non-invasive imaging technique for diagnosing and monitoring retinal diseases like diabetic retinopathy, age-related macular degeneration, and glaucoma. Current 2D-based methods for retinal vessel (RV) segmentation offer insufficient accuracy. To address this, we propose RVMamba, a novel architecture integrating multiple feature extraction modules with the Mamba state-space model. Moreover, existing joint segmentation models for OCTA data exhibit performance imbalance between different tasks. To simultaneously improve the segmentation of the foveal avascular zone (FAZ) and mitigate this imbalance, we introduce FAZMamba and a unified Joint-OCTAMamba framework. Experimental results on the OCTA-500 dataset demonstrate that Joint-OCTAMamba outperforms existing models across evaluation metrics.The code is available at https://github.com/lc-sfis/Joint-OCTAMamba.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Mamba for medical image analysis, one of 10 directions in State-space sequence models (S4, Mamba). Builds on 16 of its 26 references that are in the map. First posted on arXiv 15 Sep 2025.
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Other references · 15
- 2017Attention Is All You Needcited by 60,361
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2023Segment Anythingcited by 8,234
- 2016Feature Pyramid Networks for Object Detectioncited by 5,036
- 2017Squeeze-and-Excitation Networkscited by 4,789
- 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,718
- 2017Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learningcited by 950
- 2019ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networkscited by 575
- 2018Albumentations: fast and flexible image augmentationscited by 560
- 2019GhostNet: More Features from Cheap Operationscited by 410
- 2023Dynamic Snake Convolution based on Topological Geometric Constraints for Tubular Structure Segmentationcited by 84
- 2020OCTA-500: A Retinal Dataset for Optical Coherence Tomography Angiography Studycited by 52
3 more
- 2023SAM-OCTA: A Fine-Tuning Strategy for Applying Foundation Model to OCTA Image Segmentation Taskscited by 14
- 2024OCTAMamba: A State-Space Model Approach for Precision OCTA Vasculature Segmentationcited by 3
- 2024SAM-OCTA2: Layer Sequence OCTA Segmentation with Fine-tuned Segment Anything Model 2cited by 3
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- 2026An Ultra-Widefield Swept-Source OCTA Dataset and a Polar-Gated Mamba Network for Retinal Vessel Segmentation
- 2026U-PEN Mamba: Progressive Expansion with Selective State-Space Modeling for Efficient Retinal Vessel Segmentation
- 2026Polygon-mamba: Retinal vessel segmentation using polygon scanning mamba and space-frequency collaborative attention
- 2024Serp-Mamba: Advancing High-Resolution Retinal Vessel Segmentation with Selective State-Space Model
- 2026RetiWave-Mamba: A Dual-Stream Network for Retinal Disease Detection based on Multi-scale Context and Frequency-Adaptive Mamba Projection
- 2025MM-UNet: Morph Mamba U-shaped Convolutional Networks for Retinal Vessel Segmentation
- 2025VAMPIRE: Uncovering Vessel Directional and Morphological Information from OCTA Images for Cardiovascular Disease Risk Factor Prediction
- 2025A Novel Hybrid Approach for Retinal Vessel Segmentation with Dynamic Long-Range Dependency and Multi-Scale Retinal Edge Fusion Enhancement